Transcript Linked_Sensor_Data

Publishing Linked Sensor Data
Semantic Sensor Networks Workshop 2010
In conjunction with the 9th International Semantic Web Conference
(ISWC 2010), 7-11 November 2010, Shanghai, China.
Presenter: Kerry Taylor, CSIRO ICT Centre, Canberra, Australia
Payam Barnaghi (Uni. of Surrey), Mirko Presser (Alexandra Institute), Klaus Moessner
(Uni. of Surrey)
Contact author: Payam Barnaghi ([email protected])
Centre for Communication Systems Research
University of Surrey
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Sensor networks and accessing
physical world data
• There are currently ongoing research on creating large-scale
sensor/actuator networks; This will enable connecting millions of
devices that capture physical world data in a global scale.
• The sensors provide observation and measurement data from the
physical.
• The current data transmission on sensor networks mostly relies on
binary or syntactic data models which lack of providing machine
interpretable meanings to the data.
– Binary representation or in some cases XML-based data
– No general agreement
– Requires an pre-agreement on both communication parties to be able to
process and interpret the data
– Limited reasoning
– Limited interoperability
– Data integration and fusion issues
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Physical world data on the Web
• The idea is providing sensor data on the same level as
the Web data.
– Semantic enrichment of data and integrating the real world data
into the digital world;
• Providing annotations and associating the descriptions to
existing ontologies and domain knowledge
• There are existing standards such as those provided by
OGC, SSN-XG Sensor Ontology,…
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W3C SSN-XG ontology
SSN-XG annotations
makes observations
of this type
What it
measures
units
SSN-XG ontologies
where it is
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Sensor ontologies and semantic data
• The ontologies and semantic models provide machineinterpretable descriptions
• There is no direct association to the domain knowledge
– What a sensor measures, where it is, etc.
– Association of an observation and/or measurement data to a
feature of interest.
• Including the domain knowledge and relating the
enriched description to the existing data in the digital
world will support semantic integration.
• Inference mechanism can process and analyse the
emerging semantics.
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Semantic interoperability and semantic
integration
Making sensor-generated information usable as
a new and key source of knowledge will require
their integration into the (existing) information
space of Communities  Semantic Integration
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Semantic integration- example
“I am a parcel
for Tom,
dropped once”
1010
1010
1010
1010
1010
1010
Middleware
“I am a Post
van, not
going to
Tom”
Middleware
“I am TWITTER”
Middleware
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Semantic
Semantic
Middleware
Middleware
Middleware
Mash-up of
Real World
Knowledge
Semantic
Semantic integration- example
Description
Discovery
Integration
Distributed processing
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Semantic integration
• Semantics allows to create reusable knowledge that
helps to
– understand who is talking to whom
– who is doing what
– and what the information means
• This enables the integration of information as
knowledge.
• On a large scale this machine interpretable data is a key
enabler and a necessity for the Real World Internet.
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Publishing linked sensor data
• Using existing knowledge on the Web to annotate the
sensor resources.
• Associating sensor descriptions to the domain
knowledge.
• Defining links between sensor observation and
measurement and features of interests using the existing
knowledge and domain ontologies.
• Making sensor descriptions as a part of Web data and
accessible through standard interfaces.
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Linked data principles
• The principles in designing the linked data are defined
as:
– using URI’s as names for things;
– using HTTP URI’s to enable people to look up those names;
– provide useful RDF information related to URI’s that are looked
up by machine or people;
– including RDF statements that link to other URI’s to enable
discovery of other related things of the web of data;
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Linked Data- Connecting distributed data across
the Web
- There are more than 13.1 billion interlinked RDF triples.
- more than 142 million RDF links (properties).
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Sensor data and linked data
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* The middle layer is adapted from Amit Sheth et al., “Semantic Sensor Web”
Publishing linked sensor data
• We use existing linked-data to annotate sensor data and
to associate the description to the domain knowledge;
• We also publish the sensor data a linked data resources.
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Using linked data for annotation
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Using linked data for annotation –
location model
• We have a two layers location description;
• A detailed location ontology for local descriptions;
• A location attribute (concept) obtained from linked data
(e.g. DBPedia, GeoNames);
• The local ontology provides detailed location description
(e.g. rooms, buildings on our campus) and the linked
data concepts provide high-level concepts (e.g.
University of Surrey) and then we linked these two
models;
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Using linked data for annotation –
location model
Internal location
ontology (local)
Lined-data location
(external) 17
Using and reasoning the publishing
linked sensor data
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Components and architecture
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The World at your Fingertips
The world is the knowledge
base
Despite data volume, heterogeneity, distribution, dynamics:
Integration/access all that data like a set of interconnected
resources in an information network!
- Structured Querying
- Integrated Views
- Aggregation, Analyses  Reasoning upon the data
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Thank you!
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